递归神经网络深度学习研究综述

M. Kaur, Aakash Mohta
{"title":"递归神经网络深度学习研究综述","authors":"M. Kaur, Aakash Mohta","doi":"10.1109/ICSSIT46314.2019.8987837","DOIUrl":null,"url":null,"abstract":"Recurrent Neural Network (RNN) is a deep learning model that uses the concept of supervised learning. Deep learning belongs to the family of machine learning. It is also called hierarchical learning or deep structured learning. The classic machine learning algorithms are definite, while the deep learning algorithms follow a chain of command. Deep learning has the capability to deal with more complex neural networks and it mainly deals with sequential data. Recurrent networks can process examples one at a time, preserving an element, that reflects over a long period of time.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"A Review of Deep Learning with Recurrent Neural Network\",\"authors\":\"M. Kaur, Aakash Mohta\",\"doi\":\"10.1109/ICSSIT46314.2019.8987837\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recurrent Neural Network (RNN) is a deep learning model that uses the concept of supervised learning. Deep learning belongs to the family of machine learning. It is also called hierarchical learning or deep structured learning. The classic machine learning algorithms are definite, while the deep learning algorithms follow a chain of command. Deep learning has the capability to deal with more complex neural networks and it mainly deals with sequential data. Recurrent networks can process examples one at a time, preserving an element, that reflects over a long period of time.\",\"PeriodicalId\":330309,\"journal\":{\"name\":\"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSIT46314.2019.8987837\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSIT46314.2019.8987837","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25

摘要

递归神经网络(RNN)是一种使用监督学习概念的深度学习模型。深度学习属于机器学习的家族。它也被称为分层学习或深度结构化学习。经典的机器学习算法是明确的,而深度学习算法遵循命令链。深度学习有能力处理更复杂的神经网络,它主要处理序列数据。循环网络可以一次处理一个例子,保留一个元素,这反映了很长一段时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Deep Learning with Recurrent Neural Network
Recurrent Neural Network (RNN) is a deep learning model that uses the concept of supervised learning. Deep learning belongs to the family of machine learning. It is also called hierarchical learning or deep structured learning. The classic machine learning algorithms are definite, while the deep learning algorithms follow a chain of command. Deep learning has the capability to deal with more complex neural networks and it mainly deals with sequential data. Recurrent networks can process examples one at a time, preserving an element, that reflects over a long period of time.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信